An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding | Paper Citation Record

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This paper cites In: 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp 260--271.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp 260--271

Reference 1

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This paper cites (2022) Gpt-neox-20b: An open-source autoregressive language model.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2022) Gpt-neox-20b: An open-source autoregressive language model

Reference 2

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This paper cites (2020) Language models are few-shot learners.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2020) Language models are few-shot learners

Reference 3

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This paper cites Neuron 112(5):698--717.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Neuron 112(5):698--717

Reference 4

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This paper cites Communications of the ACM 54(4):142--151.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Communications of the ACM 54(4):142--151

Reference 5

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This paper cites arXiv preprint arXiv:231014735.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:231014735

Reference 6

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This paper cites In: 2023 20th Annual International Conference on Privacy, Security and Trust (PST), IEEE, pp 1--11.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2023 20th Annual International Conference on Privacy, Security and Trust (PST), IEEE, pp 1--11

Reference 7

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This paper cites (2021) Evaluating large language models trained on code.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2021) Evaluating large language models trained on code

Reference 8

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Reference 9

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This paper cites Nature Communications 15(1):1418.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Nature Communications 15(1):1418

Reference 10

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This paper cites arXiv preprint arXiv:221210559.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:221210559

Reference 11

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This paper cites Proceedings of the ACM on Programming Languages 4(OOPSLA):1--28.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Proceedings of the ACM on Programming Languages 4(OOPSLA):1--28

Reference 12

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Proceedings of the ACM on Programming Languages 4(OOPSLA):1--28

Reference 13

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Reference 14

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This paper cites (2020) Codebert: A pre-trained model for programming and natural languages.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2020) Codebert: A pre-trained model for programming and natural languages

Reference 15

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:220405999

Reference 17

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This paper cites In: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp 607--619.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp 607--619

Reference 18

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Reference 19

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This paper cites In: Network and Distributed System Security Symposium.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Network and Distributed System Security Symposium

Reference 20

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This paper cites (2024) Deepseek-coder: When the large language model meets programming--the rise of code intelligence.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2024) Deepseek-coder: When the large language model meets programming--the rise of code intelligence

Reference 21

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This paper cites In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp 1667--1680.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp 1667--1680

Reference 22

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding https://www.hex-rays.com/products/ ida

Reference 23

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding ACM Transactions on Software Engineering and Methodology 33(8):1--79

Reference 24

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2022) Lora: Low-rank adaptation of large language models

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This paper cites arXiv preprint arXiv:190909436.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:190909436

Reference 27

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Reference 28

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding https://dwarfstd.org/doc/DWARF4.pdf

Reference 29

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 935--944

Reference 30

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2024) Mixtral of experts

Reference 31

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp 1631--1645

Reference 32

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2015 ieee/acm 1st international workshop on software protection, IEEE, pp 3--9

Reference 33

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This paper cites IEEE Transactions on Software Engineering 49(4):1661--1682.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding IEEE Transactions on Software Engineering 49(4):1661--1682

Reference 34

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:230807702

Reference 35

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 66--75

Reference 36

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Machine translation 23:105--115

Reference 37

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 2021 ACM SIGSAC conference on computer and communications security, pp 3236--3251

Reference 38

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 39

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 40

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Text summarization branches out, pp 74--81

Reference 41

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Reference 42

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This paper cites arXiv preprint arXiv:240618379.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:240618379

Reference 43

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:230608568

Reference 44

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, IEEE, vol 2, pp 951--952

Reference 45

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Reference 46

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Computational Intelligence and Neuroscience 2022(1):6294058

Reference 47

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding https://github.com/NationalSecurityAgency/ghidra

Reference 48

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:230502309

Reference 49

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Reference 50

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2022) Training language models to follow instructions with human feedback

Reference 51

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An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311--318

Reference 52

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This paper cites In: 2023 IEEE Symposium on Security and Privacy (SP), IEEE, pp 2375--2390.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2023 IEEE Symposium on Security and Privacy (SP), IEEE, pp 2375--2390

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.718341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 64b98fb4-3e15-45bb-b340-b023b9d5dd60 · outbound

This paper cites arXiv preprint arXiv:201208680.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:201208680

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.689412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6bbbf48-dfc1-4763-8227-8ff9d9438bd0 · outbound

This paper cites an unresolved cited work.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:58:56.665750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation edcaa93c-494a-420c-a5c0-e98fa4113932 · outbound

This paper cites an unresolved cited work.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:58:56.638571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 86342287-0a8e-447d-b4cf-a41f472520a2 · outbound

This paper cites (2023) Code llama: Open foundation models for code.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2023) Code llama: Open foundation models for code

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.377574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 290f631f-e717-4c9c-91ae-dfc1daeef0c0 · outbound

This paper cites ACM Transactions on Software Engineering and Methodology.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding ACM Transactions on Software Engineering and Methodology

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.588911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc43c938-d4e3-49b2-87b2-005b394655e0 · outbound

This paper cites In: 2024 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, pp 1--12.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2024 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, pp 1--12

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.556757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8cec3f2b-ab9e-4b66-b53b-ec53a80bdcf6 · outbound

This paper cites Proceedings of the ACM on Software Engineering 1(FSE):47--69.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Proceedings of the ACM on Software Engineering 1(FSE):47--69

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.522023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32eb1678-31c7-4384-a295-0b5e4777497d · outbound

This paper cites In: Proceedings of the 25th IEEE/ACM international conference on Automated software engineering, pp 43--52.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 25th IEEE/ACM international conference on Automated software engineering, pp 43--52

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.412576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1c327f3-afce-4dbf-bb86-f75f5ecfae95 · outbound

This paper cites In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp 930--957.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp 930--957

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.468979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7028d873-d6da-4d75-b31b-b708f3197d78 · outbound

This paper cites (2023 a ) Llama: Open and efficient foundation language models.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2023 a ) Llama: Open and efficient foundation language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.439766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56139fef-e709-42d2-bb8f-5519174f3721 · outbound

This paper cites (2023 b ) Llama 2: Open foundation and fine-tuned chat models.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2023 b ) Llama 2: Open foundation and fine-tuned chat models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.416429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b6e3b46e-f3db-4a30-bf8d-c4c471dca7d1 · outbound

This paper cites an unresolved cited work.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:58:56.386303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6baeee09-3a3c-4508-b696-843db0f2bedb · outbound

This paper cites an unresolved cited work.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:58:56.355964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b97c490-fb58-4cc7-8b4e-536b216b58e9 · outbound

This paper cites binary ninja.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding binary ninja

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.332647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1cb88afa-37cb-4752-87dd-008c435db0fc · outbound

This paper cites In: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, pp 1--13.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, pp 1--13

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.311242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f514bb14-289b-46dc-8931-8a8e0ffec97c · outbound

This paper cites arXiv preprint arXiv:210900859.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:210900859

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.278810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11ef687c-76f6-4839-a811-d3e676499136 · outbound

This paper cites In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp 1069--1088.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp 1069--1088

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.253789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e5c9ab9-84e7-4751-aaae-6b72d43ce97e · outbound

This paper cites an unresolved cited work.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:58:56.221514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d256fb84-2d21-4ced-9bcb-e30c0b7e7409 · outbound

This paper cites In: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, pp 1282--1294.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, pp 1282--1294

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.196676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 58c06883-3fdb-465a-b2c5-a526b36e5967 · outbound

This paper cites In: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp 774--786.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp 774--786

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.172025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b748cfd-113f-4946-a7fa-a83b020a25b2 · outbound

This paper cites In: Proceedings of the 6th ACM SIGPLAN international symposium on machine programming, pp 1--10.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 6th ACM SIGPLAN international symposium on machine programming, pp 1--10

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.148507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c70d12ef-408b-448f-bedd-2f143933ef81 · outbound

This paper cites In: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), IEEE, pp 462--472.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), IEEE, pp 462--472

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.116633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c45d5673-59ae-4e55-94f3-21c6704b7187 · outbound

This paper cites arXiv preprint arXiv:231016853.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:231016853

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.090624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13cb4336-5e87-450d-b52c-fed34c78e8ea · outbound

This paper cites ACM Transactions on Software Engineering and Methodology (TOSEM) 4(2):146--170.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding ACM Transactions on Software Engineering and Methodology (TOSEM) 4(2):146--170

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.062845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c9d1078-8682-43be-b8e0-dc0fc9715d93 · outbound

This paper cites (2022) Glm-130b: An open bilingual pre-trained model.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2022) Glm-130b: An open bilingual pre-trained model

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.036742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b79f83b-26a2-4004-8ae2-fa656fc362fa · outbound

This paper cites In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp 1--3.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp 1--3

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:56.010891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5467c14a-da56-4655-a515-5c7a9d46b23a · outbound

This paper cites (2023) How well does llm generate security tests? arXiv preprint arXiv:231000710.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2023) How well does llm generate security tests? arXiv preprint arXiv:231000710

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:55.981576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4a4b9558-0518-47f0-8d38-94026e93c586 · outbound

This paper cites In: 2021 IEEE Symposium on Security and Privacy (SP), IEEE, pp 659--676.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: 2021 IEEE Symposium on Security and Privacy (SP), IEEE, pp 659--676

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:55.959311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5a77c2df-c088-4318-946b-e24faa39a094 · outbound

This paper cites (2023) Judging llm-as-a-judge with mt-bench and chatbot arena.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding (2023) Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.599219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43aacfbd-1964-4c22-be49-8a8820f95d90 · outbound

This paper cites arXiv preprint arXiv:240313372.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding arXiv preprint arXiv:240313372

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:55.911894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f36329f1-effb-47c4-94b6-8c8dc373b98e · outbound

This paper cites In: Companion Proceedings of the 33nd ACM International Conference on the Foundations of Software Engineering.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding In: Companion Proceedings of the 33nd ACM International Conference on the Foundations of Software Engineering

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:58:55.890050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 940d80f8-f9e2-47af-97f3-39de0226b452 · outbound

This paper cites an unresolved cited work.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:58:55.862479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 779f903d-c22d-43a6-bccd-c393d8c64c73 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding , " * write output.state after.block = add.period write newline

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.635110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5272126f-6b90-48e8-be21-9ff21a30a368 · outbound

This paper cites write newline.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding write newline

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.645388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7996bcc1-ded1-4b41-8a8b-f137da7a3b0f · outbound

This paper cites , " * write output.state after.block = add.period write newline.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding , " * write output.state after.block = add.period write newline

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.655561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4acc4f2d-e79c-4a0f-9019-cf47714f659a · outbound

This paper cites write newline.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding write newline

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.665470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98102433-dc1f-4c85-8b77-a09a0c7f63f5 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding , " * write output.state after.block = add.period write newline

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.671784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 856b96a3-8200-4d67-b65e-034b2bdf2baa · outbound

This paper cites write newline.

An Empirical Study on the Effectiveness of Large Language Models for Binary Code Understanding write newline

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-16T04:58:55.678189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers